class_docstr: Base class of stacking classifiers
code: def _do_predict(self, X, predict_fn):
      meta_features = self.predict_meta_features(X)
  
      if not self.use_features_in_secondary:
          return predict_fn(meta_features)
      elif sparse.issparse(X):
          return predict_fn(sparse.hstack((X, meta_features)))
      else:
          return predict_fn(np.hstack((X, meta_features)))
  
file_path: mlxtend/mlxtend/classifier/_base_classification.py
imports: import numpy
  from scipy import sparse
  from sklearn.base import ClassifierMixin
  from ..externals.estimator_checks import check_is_fitted
label_desc: Use image node_id 1 for calling the _BaseStackingClassifier obj's underlying member method code with example usage: obj._do_predict(X, predict_fn) and returns: predict_fn, predict_fn, predict_fn